Abstract
An improved radial basis function neural network is proposed that preprocessing is the key to improving the precision of short-term load forecasting. This paper presents a new model which is based on classical RBF neural network, combine the GA-optimized SVM radial basis function and RBF neural network. According to the date of the type, temperature, weather conditions and other factors ,The Application of combined GA-optimized SVM radial basis function is used to extract useful data to improve the load forecasting accuracy of RBF neural network. Spring load data of California were applied for simulation. The simulation indicates that the new method is feasible and the forecasting precision is greatly improved.
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